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<a href="_cross_entropy_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Error measure for classification tasks that can be used</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * as the objective function for training.</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * </span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * </span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * </span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> *</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * \author      -</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * \date        -</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> *</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> *</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * 3</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * </span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * </span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment"> *</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment"> */</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#ifndef SHARK_OBJECTIVEFUNCTIONS_LOSS_CROSS_ENTROPY_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#define SHARK_OBJECTIVEFUNCTIONS_LOSS_CROSS_ENTROPY_H</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_loss_8h.html" title="super class of all loss functions">shark/ObjectiveFunctions/Loss/AbstractLoss.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span> </div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>{</div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment"></span> </div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">///  \brief Error measure for classification tasks that can be used</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">///         as the objective function for training.</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">///</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">///  If your model should return a vector whose components reflect the</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">///  logarithmic conditional probabilities of class membership given any input vector</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">///  &#39;CrossEntropy&#39; is the adequate error measure for model-training.</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">///  For \em C&gt;1 classes the loss function is defined as</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">///  \f[</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">///      E = - \ln \frac{\exp{x_c}} {\sum_{c^{\prime}=1}^C \exp{x_c^{\prime}}} = - x_c + \ln \sum_{c^{\prime}=1}^C \exp{x_c^{\prime}} </span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">///  \f]</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">///  where \em x is the prediction vector of the model and \em c is the class label. In the case of only one</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">///  model output and binary classification, another more numerically stable formulation is used:</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">///  \f[</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">///     E = \ln(1+ e^{-yx})</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">///  \f]</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">///  here, \em y are class labels between -1 and 1 and y = -2 c+1. The reason why this is numerically more stable is,</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">///  that when \f$ e^{-yx} \f$ is big, the error function is well approximated by the linear function \em x. Also if</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">///  the exponential is very small, the case \f$ \ln(0) \f$ is avoided.</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">///</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">/// If the class labels are integers, they must be starting from 0. If class labels are vectors, there must be a proper</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">/// probability vector. i.e. values must be bigger or equal to zero and sum to one. This incldues one-hot-encoding of labels.</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">/// Also for theoretical reasons, the output neurons of a neural Network that is trained with this loss should be linear.</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">/// \ingroup lossfunctions</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment"></span> </div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> LabelType, <span class="keyword">class</span> OutputType&gt;</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy.html">   68</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_cross_entropy.html" title="Error measure for classification tasks that can be used as the objective function for training.">CrossEntropy</a>;</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span> </div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span> </div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> OutputType&gt;</div>
<div class="foldopen" id="foldopen00072" data-start="{" data-end="};">
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html">   72</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_cross_entropy.html" title="Error measure for classification tasks that can be used as the objective function for training.">CrossEntropy</a>&lt;unsigned int, <a class="code hl_typedef" href="classshark_1_1_abstract_loss.html#aff632efe5055d1f07de94a790b222b85">OutputType</a>&gt; : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_loss.html" title="Loss function interface.">AbstractLoss</a>&lt;unsigned int,OutputType&gt;</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>{</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_loss.html" title="Loss function interface.">AbstractLoss&lt;unsigned int,OutputType&gt;</a> <a class="code hl_class" href="classshark_1_1_abstract_loss.html">base_type</a>;</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_loss.html#ac52e23c4acfdb2d08b55420101eee787" title="Const references to LabelType.">base_type::ConstLabelReference</a> ConstLabelReference;</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_loss.html#a50b1635725e3a6bbb6017a6e3c4a52ca" title="Const references to OutputType.">base_type::ConstOutputReference</a> ConstOutputReference;</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_loss.html#ac3a1a01831f11b5357d6005837ac245b">base_type::BatchOutputType</a> BatchOutputType;</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_loss.html#a87fa1fa41bb3c1d5ce75137428724536">base_type::MatrixType</a> <a class="code hl_typedef" href="_mc_svm_linear_8cpp.html#a88ab98d46276376a56c2a396842cd58e">MatrixType</a>;</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span> </div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>    <span class="comment">//uses different formula to compute the binary case for 1 output.</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>    <span class="comment">//should be numerically more stable</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>    <span class="comment">//formula: ln(1+exp(-yx)) with y = -1/1</span></div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>    <span class="keywordtype">double</span> evalError(<span class="keywordtype">double</span> label,<span class="keywordtype">double</span> exponential,<span class="keywordtype">double</span> value)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span> </div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        <span class="keywordflow">if</span>(value*label &lt; -200 ){</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>            <span class="comment">//below this, we might get numeric instabilities</span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>            <span class="comment">//but we know, that ln(1+exp(x)) converges to x for big arguments</span></div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>            <span class="keywordflow">return</span> - value * label;</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        }</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>        <span class="keywordflow">return</span> std::log(1+exponential);</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>    }</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span><span class="keyword">public</span>:</div>
<div class="foldopen" id="foldopen00094" data-start="{" data-end="}">
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#ab3f85fac7c5cb438eeecd05d4e6a07db">   94</a></span>    <a class="code hl_function" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#ab3f85fac7c5cb438eeecd05d4e6a07db">CrossEntropy</a>()</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    { this-&gt;m_features |= base_type::HAS_FIRST_DERIVATIVE;}</div>
</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span> </div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment"></span> </div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00099" data-start="{" data-end="}">
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#aa508dd3b0e9621775385942be62d72fd">   99</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#aa508dd3b0e9621775385942be62d72fd" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;CrossEntropy&quot;</span>; }</div>
</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span> </div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    <span class="comment">// annoyingness of C++ templates</span></div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    <span class="keyword">using </span>base_type::eval;</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span> </div>
<div class="foldopen" id="foldopen00105" data-start="{" data-end="}">
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#a00c328589910ce17ecd13a57b59d6a4c">  105</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#a00c328589910ce17ecd13a57b59d6a4c">eval</a>(UIntVector <span class="keyword">const</span>&amp; target, BatchOutputType <span class="keyword">const</span>&amp; prediction)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        <span class="keywordtype">double</span> error = 0;</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != prediction.size1(); ++i){</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>            error += eval(target(i), row(prediction,i));</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        }</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>        <span class="keywordflow">return</span> error;</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>    }</div>
</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>    </div>
<div class="foldopen" id="foldopen00113" data-start="{" data-end="}">
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#a07fbea8cabdb5f573d4376e7bfda77e0">  113</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#a07fbea8cabdb5f573d4376e7bfda77e0" title="evaluate the loss for a target and a prediction">eval</a>( ConstLabelReference target, ConstOutputReference prediction)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        <span class="keywordflow">if</span> ( prediction.size() == 1 )</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        {</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>            <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a> ( target &lt; 2 );</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>            <span class="keywordtype">double</span> label = 2.0 * target - 1;   <span class="comment">//converts labels from 0/1 to -1/1</span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>            <span class="keywordtype">double</span> exponential =  std::exp( -label * prediction(0) );</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>            <span class="keywordflow">return</span> evalError(label,exponential,prediction(0 ));</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        }<span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>            <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a> ( target &lt; prediction.size() );</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>            </div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>            <span class="comment">//calculate the log norm in a numerically stable way</span></div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>            <span class="comment">//we subtract the maximum prior to exponentiation to </span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>            <span class="comment">//ensure that the exponentiation result will still fit in double</span></div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>            <span class="keywordtype">double</span> maximum = max(prediction);</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>            <span class="keywordtype">double</span> logNorm = sum(exp(prediction-maximum));</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>            logNorm = std::log(logNorm) + maximum;</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>            <span class="keywordflow">return</span> logNorm - prediction(target);</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>        }</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>    }</div>
</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span> </div>
<div class="foldopen" id="foldopen00133" data-start="{" data-end="}">
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#a960711d6a29da49c4eb53aaffc398ef8">  133</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#a960711d6a29da49c4eb53aaffc398ef8">evalDerivative</a>(UIntVector <span class="keyword">const</span>&amp; target, BatchOutputType <span class="keyword">const</span>&amp; prediction, BatchOutputType&amp; gradient)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        gradient.resize(prediction.size1(),prediction.size2());</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <span class="keywordflow">if</span> ( prediction.size2() == 1 )</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        {</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>            <span class="keywordtype">double</span> error = 0;</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>            <span class="keywordflow">for</span>(std::size_t i = 0; i != prediction.size1(); ++i){</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>                <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a> ( target(i) &lt; 2 );</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>                <span class="keywordtype">double</span> label = 2 * <span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(target(i)) - 1;   <span class="comment">//converts labels from 0/1 to -1/1</span></div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>                <span class="keywordtype">double</span> exponential =  std::exp ( -label * prediction (i, 0 ) );</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>                <span class="keywordtype">double</span> <a class="code hl_function" href="group__shark__globals.html#ga28f3c1d61cf1c070d4687d03bc5d99e9" title="Logistic function/logistic function.">sigmoid</a> = 1.0/(1.0+exponential);</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>                gradient ( i,0 ) = -label * (1.0 - <a class="code hl_function" href="group__shark__globals.html#ga28f3c1d61cf1c070d4687d03bc5d99e9" title="Logistic function/logistic function.">sigmoid</a>);</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>                error+=evalError(label,exponential,prediction (i, 0 ));</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>            }</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>            <span class="keywordflow">return</span> error;</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>        }<span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>            <span class="keywordtype">double</span> error = 0;</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>            <span class="keywordflow">for</span>(std::size_t i = 0; i != prediction.size1(); ++i){</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>                <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a> ( target(i) &lt; prediction.size2() );</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>                <span class="keyword">auto</span> gradRow=row(gradient,i);</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>                </div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>                <span class="comment">//calculate the log norm in a numerically stable way</span></div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>                <span class="comment">//we subtract the maximum prior to exponentiation to </span></div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>                <span class="comment">//ensure that the exponentiation result will still fit in double</span></div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>                <span class="comment">//this does not change the result as the values get normalized by</span></div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>                <span class="comment">//their sum and thus the correction term cancels out.</span></div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>                <span class="keywordtype">double</span> maximum = max(row(prediction,i));</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>                noalias(gradRow) = exp(row(prediction,i) - maximum);</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>                <span class="keywordtype">double</span> norm = sum(gradRow);</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>                gradRow/=norm;</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>                gradient(i,target(i)) -= 1;</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>                error+=std::log(norm) - prediction(i,target(i))+maximum;</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>            }</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>            <span class="keywordflow">return</span> error;</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        }</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>    }</div>
</div>
<div class="foldopen" id="foldopen00168" data-start="{" data-end="}">
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#a2377944f46db78d25811b70a9b8175ad">  168</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#a2377944f46db78d25811b70a9b8175ad" title="evaluate the loss and its derivative for a target and a prediction">evalDerivative</a>(ConstLabelReference target, ConstOutputReference prediction, <a class="code hl_typedef" href="classshark_1_1_abstract_loss.html#aff632efe5055d1f07de94a790b222b85">OutputType</a>&amp; gradient)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        gradient.resize(prediction.size());</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        <span class="keywordflow">if</span> ( prediction.size() == 1 ){</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>            <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a> ( target &lt; 2 );</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>            <span class="keywordtype">double</span> label = 2.0 * target - 1;   <span class="comment">//converts labels from 0/1 to -1/1</span></div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>            <span class="keywordtype">double</span> exponential =  std::exp ( - label * prediction(0));</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>            <span class="keywordtype">double</span> <a class="code hl_function" href="group__shark__globals.html#ga28f3c1d61cf1c070d4687d03bc5d99e9" title="Logistic function/logistic function.">sigmoid</a> = 1.0/(1.0+exponential);</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>            gradient(0) = -label * (1.0 - <a class="code hl_function" href="group__shark__globals.html#ga28f3c1d61cf1c070d4687d03bc5d99e9" title="Logistic function/logistic function.">sigmoid</a>);</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>            <span class="keywordflow">return</span> evalError(label,exponential,prediction(0));</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>        }<span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>            <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a> ( target &lt; prediction.size() );</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>            </div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>            <span class="comment">//calculate the log norm in a numerically stable way</span></div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>            <span class="comment">//we subtract the maximum prior to exponentiation to </span></div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>            <span class="comment">//ensure that the exponentiation result will still fit in double</span></div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>            <span class="comment">//this does not change the result as the values get normalized by</span></div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>            <span class="comment">//their sum and thus the correction term cancels out.</span></div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            <span class="keywordtype">double</span> maximum = max(prediction);</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>            noalias(gradient) = exp(prediction - maximum);</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>            <span class="keywordtype">double</span> norm = sum(gradient);</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>            gradient /= norm;</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>            gradient(target) -= 1;</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>            <span class="keywordflow">return</span> std::log(norm) - prediction(target) + maximum;</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>        }</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>    }</div>
</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span> </div>
<div class="foldopen" id="foldopen00194" data-start="{" data-end="}">
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#ac70b304cdf2fb8eee5f8d1aa3047b1cf">  194</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_3_01unsigned_01int_00_01_output_type_01_4.html#ac70b304cdf2fb8eee5f8d1aa3047b1cf">evalDerivative</a>(</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>        ConstLabelReference target, ConstOutputReference prediction,</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>        BatchOutputType&amp; gradient,MatrixType &amp; hessian</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>    )<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>        gradient.resize(prediction.size());</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>        hessian.resize(prediction.size(),prediction.size());</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>        <span class="keywordflow">if</span> ( prediction.size() == 1 )</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>        {</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>            <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a> ( target &lt; 2 );</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>            <span class="keywordtype">double</span> label = 2 * <span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(target) - 1;   <span class="comment">//converts labels from 0/1 to -1/1</span></div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>            <span class="keywordtype">double</span> exponential =  std::exp ( -label * prediction ( 0 ) );</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>            <span class="keywordtype">double</span> <a class="code hl_function" href="group__shark__globals.html#ga28f3c1d61cf1c070d4687d03bc5d99e9" title="Logistic function/logistic function.">sigmoid</a> = 1.0/(1.0+exponential);</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>            gradient ( 0 ) = -label * (1.0-<a class="code hl_function" href="group__shark__globals.html#ga28f3c1d61cf1c070d4687d03bc5d99e9" title="Logistic function/logistic function.">sigmoid</a>);</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>            hessian ( 0,0 ) = <a class="code hl_function" href="group__shark__globals.html#ga28f3c1d61cf1c070d4687d03bc5d99e9" title="Logistic function/logistic function.">sigmoid</a> * ( 1-<a class="code hl_function" href="group__shark__globals.html#ga28f3c1d61cf1c070d4687d03bc5d99e9" title="Logistic function/logistic function.">sigmoid</a> );</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>            <span class="keywordflow">return</span> evalError(label,exponential,prediction ( 0 ));</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>        }</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>        {</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>            <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a> ( target &lt; prediction.size() );</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>            <span class="comment">//calculate the log norm in a numerically stable way</span></div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>            <span class="comment">//we subtract the maximum prior to exponentiation to </span></div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>            <span class="comment">//ensure that the exponentiation result will still fit in double</span></div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>            <span class="comment">//this does not change the result as the values get normalized by</span></div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>            <span class="comment">//their sum and thus the correction term cancels out.</span></div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>            <span class="keywordtype">double</span> maximum = max(prediction);</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>            noalias(gradient) = exp(prediction-maximum);</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span>            <span class="keywordtype">double</span> norm = sum(gradient);</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>            gradient/=norm;</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span> </div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>            noalias(hessian)=-outer_prod(gradient,gradient);</div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>            noalias(diag(hessian)) += gradient;</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>            gradient(target) -= 1;</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span> </div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>            <span class="keywordflow">return</span> std::log(norm) - prediction(target) - maximum;</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>        }</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>    }</div>
</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>};</div>
</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span> </div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span> </div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> T, <span class="keyword">class</span> Device&gt;</div>
<div class="foldopen" id="foldopen00234" data-start="{" data-end="};">
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01blas_1_1vector_3_01_t_00_01_device_01_4_00_01blas_1_1vector_3_01_t_00_01_device_01_4_01_4.html">  234</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_cross_entropy.html" title="Error measure for classification tasks that can be used as the objective function for training.">CrossEntropy</a>&lt;blas::vector&lt;T, Device&gt;, blas::vector&lt;T, Device&gt; &gt;</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>: <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_loss.html" title="Loss function interface.">AbstractLoss</a>&lt;blas::vector&lt;T, Device&gt;, blas::vector&lt;T, Device&gt;&gt;</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>{</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>    <span class="keyword">typedef</span> blas::vector&lt;T, Device&gt; OutputType;</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_loss.html" title="Loss function interface.">AbstractLoss&lt;OutputType,OutputType&gt;</a> <a class="code hl_class" href="classshark_1_1_abstract_loss.html" title="Loss function interface.">base_type</a>;</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_loss.html#ac52e23c4acfdb2d08b55420101eee787" title="Const references to LabelType.">base_type::ConstLabelReference</a> ConstLabelReference;</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_loss.html#a50b1635725e3a6bbb6017a6e3c4a52ca" title="Const references to OutputType.">base_type::ConstOutputReference</a> ConstOutputReference;</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_loss.html#ac3a1a01831f11b5357d6005837ac245b">base_type::BatchOutputType</a> BatchOutputType;</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_abstract_loss.html#a87fa1fa41bb3c1d5ce75137428724536">base_type::MatrixType</a> <a class="code hl_typedef" href="_mc_svm_linear_8cpp.html#a88ab98d46276376a56c2a396842cd58e">MatrixType</a>;</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span><span class="keyword">public</span>:</div>
<div class="foldopen" id="foldopen00245" data-start="{" data-end="}">
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01blas_1_1vector_3_01_t_00_01_device_01_4_00_01blas_1_1vector_3_01_t_00_01_device_01_4_01_4.html#ab8d138b1cecc664543c1b00f07a2d492">  245</a></span>    <a class="code hl_function" href="classshark_1_1_cross_entropy_3_01blas_1_1vector_3_01_t_00_01_device_01_4_00_01blas_1_1vector_3_01_t_00_01_device_01_4_01_4.html#ab8d138b1cecc664543c1b00f07a2d492">CrossEntropy</a>()</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>    { this-&gt;m_features |= base_type::HAS_FIRST_DERIVATIVE;}</div>
</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span> </div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span><span class="comment"></span> </div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00250" data-start="{" data-end="}">
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01blas_1_1vector_3_01_t_00_01_device_01_4_00_01blas_1_1vector_3_01_t_00_01_device_01_4_01_4.html#aafe39e8c9f73cb1120d6be4de61e3a5a">  250</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_cross_entropy_3_01blas_1_1vector_3_01_t_00_01_device_01_4_00_01blas_1_1vector_3_01_t_00_01_device_01_4_01_4.html#aafe39e8c9f73cb1120d6be4de61e3a5a" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;CrossEntropy&quot;</span>; }</div>
</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span> </div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>    <span class="comment">// annoyingness of C++ templates</span></div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>    <span class="keyword">using </span>base_type::eval;</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span> </div>
<div class="foldopen" id="foldopen00256" data-start="{" data-end="}">
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01blas_1_1vector_3_01_t_00_01_device_01_4_00_01blas_1_1vector_3_01_t_00_01_device_01_4_01_4.html#a52c65eda47c78815d1e147858134dbc8">  256</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_3_01blas_1_1vector_3_01_t_00_01_device_01_4_00_01blas_1_1vector_3_01_t_00_01_device_01_4_01_4.html#a52c65eda47c78815d1e147858134dbc8">eval</a>(BatchOutputType <span class="keyword">const</span>&amp; target, BatchOutputType <span class="keyword">const</span>&amp; prediction)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(target.size1() == prediction.size1());</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(target.size2() == prediction.size2());</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>        std::size_t m = target.size2();</div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>        </div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>        OutputType maximum = max(as_rows(prediction));</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>        <span class="keyword">auto</span> <a class="code hl_function" href="group__shark__globals.html#gae47c137a0eb0ef64df529df43c456d15" title="Thresholded exp function, over- and underflow safe.">safeExp</a> = exp(prediction - trans(blas::repeat(maximum, m)));</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>        OutputType norm = sum(as_rows(<a class="code hl_function" href="group__shark__globals.html#gae47c137a0eb0ef64df529df43c456d15" title="Thresholded exp function, over- and underflow safe.">safeExp</a>));</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>        <span class="keywordtype">double</span> error = sum(log(norm)) - sum(target * prediction) + sum(maximum);</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        <span class="keywordflow">return</span> error;</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>    }</div>
</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span> </div>
<div class="foldopen" id="foldopen00268" data-start="{" data-end="}">
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"><a class="line" href="classshark_1_1_cross_entropy_3_01blas_1_1vector_3_01_t_00_01_device_01_4_00_01blas_1_1vector_3_01_t_00_01_device_01_4_01_4.html#a739430aadeb820d28ac52bd36b9b62a8">  268</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_cross_entropy_3_01blas_1_1vector_3_01_t_00_01_device_01_4_00_01blas_1_1vector_3_01_t_00_01_device_01_4_01_4.html#a739430aadeb820d28ac52bd36b9b62a8">evalDerivative</a>(BatchOutputType <span class="keyword">const</span>&amp; target, BatchOutputType <span class="keyword">const</span>&amp; prediction, BatchOutputType&amp; gradient)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>        gradient.resize(prediction.size1(),prediction.size2());</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>        std::size_t m = target.size2();</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>        </div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>        OutputType maximum = max(as_rows(prediction));</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>        noalias(gradient) = exp(prediction - trans(blas::repeat(maximum, m)));</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>        OutputType norm = sum(as_rows(gradient));</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>        noalias(gradient) /= trans(blas::repeat(norm, m));</div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span>        noalias(gradient) -= target;</div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>        <span class="keywordtype">double</span> error = sum(log(norm)) - sum(target * prediction) + sum(maximum);</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>        <span class="keywordflow">return</span> error;</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>    }</div>
</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>};</div>
</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span> </div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span> </div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>}</div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span><span class="preprocessor">#endif</span></div>
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